code: def _make_E(prefix, norm_layer, norm_kwargs):
      out = HybridConcurrent(axis=1, prefix=prefix)
      with out.name_scope():
          out.add(
              _make_branch(
                  None, norm_layer, norm_kwargs, (320, 1, None, None)
              )
          )
  
          branch_3x3 = nn.HybridSequential(prefix="")
          out.add(branch_3x3)
          branch_3x3.add(
              _make_branch(
                  None, norm_layer, norm_kwargs, (384, 1, None, None)
              )
          )
          branch_3x3_split = HybridConcurrent(axis=1, prefix="")
          branch_3x3_split.add(
              _make_branch(
                  None,
                  norm_layer,
                  norm_kwargs,
                  (384, (1, 3), None, (0, 1)),
              )
          )
          branch_3x3_split.add(
              _make_branch(
                  None,
                  norm_layer,
                  norm_kwargs,
                  (384, (3, 1), None, (1, 0)),
              )
          )
          branch_3x3.add(branch_3x3_split)
  
          branch_3x3dbl = nn.HybridSequential(prefix="")
          out.add(branch_3x3dbl)
          branch_3x3dbl.add(
              _make_branch(
                  None,
                  norm_layer,
                  norm_kwargs,
                  (448, 1, None, None),
                  (384, 3, None, 1),
              )
          )
          branch_3x3dbl_split = HybridConcurrent(axis=1, prefix="")
          branch_3x3dbl.add(branch_3x3dbl_split)
          branch_3x3dbl_split.add(
              _make_branch(
                  None,
                  norm_layer,
                  norm_kwargs,
                  (384, (1, 3), None, (0, 1)),
              )
          )
          branch_3x3dbl_split.add(
              _make_branch(
                  None,
                  norm_layer,
                  norm_kwargs,
                  (384, (3, 1), None, (1, 0)),
              )
          )
  
          out.add(
              _make_branch(
                  "avg", norm_layer, norm_kwargs, (192, 1, None, None)
              )
          )
      return out
  
file_path: gluon-cv/gluoncv/model_zoo/inception.py
imports: from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.gluon.contrib.nn import HybridConcurrent
label_desc: Use image node_id 7 for calling a global function with example usage: _make_E(prefix, norm_layer, norm_kwargs) and returns: out